The continual increase of shrub forest in the Swiss Alps over the past few decades impacts biodiversity, forest succession and the protective function of forests. Therefore, up-to-date and area-wide information on its distribution is of great interest. To detect the shrub forest areas for the whole of Switzerland (41,285 km²), we developed an approach that uses Random Forest (RF), bias correction techniques and data from multiple remote sensing sources. Manual aerial orthoimage interpretation of shrub forest areas was conducted in a non-probabilistic way to derive initial training data. The multi-sensor and open access predictor data included digital terrain and vegetation height models obtained from Airborne Laser Scanning (ALS) and stereo-imagery, as well as Synthetic Aperture Radar (SAR) backscatter from Sentinel-1 and multispectral imagery from Sentinel-2. To mitigate the expected bias due to the training data sampling strategy, two techniques using RF probability estimates were tested to improve mapping accuracy. 1) an iterative and semi-automated active learning technique was used to generate further training data and 2) threshold-moving related object growing was applied. Both techniques facilitated the production of a shrub forest map for the whole of Switzerland at a spatial resolution of 10 m. An accuracy assessment was performed using independent data covering 7640 regularly distributed National Forest Inventory (NFI) plots. We observed the influence of the bias correction techniques and found higher accuracies after each performed iteration. The Mean Absolute Error (MAE) for the predicted shrub forest proportion was reduced from 6.04% to 2.68% while achieving a Mean Bias Error (MBE) of close to 0. The present study underscores the potential of combining multi-sensor data with bias correction techniques to provide cost-effective and accurate countrywide detection of shrub forest. Moreover, the map complements currently available NFI plot sample point data.

在过去的几十年里，瑞士阿尔卑斯山灌木林的不断增加影响了生物多样性、森林演替和森林的保护功能。因此，关于其分布的最新和区域范围的信息非常有趣。检测整个瑞士的灌木林区域（41,285 km ²)，我们开发了一种使用随机森林 (RF)、偏差校正技术和来自多个遥感源的数据的方法。灌木林区域的手动航空正射影像解释是以非概率方式进行的，以获得初始训练数据。多传感器和开放存取预测器数据包括从机载激光扫描 (ALS) 和立体图像获得的数字地形和植被高度模型，以及来自 Sentinel-1 的合成孔径雷达 (SAR) 反向散射和来自 Sentinel-2 的多光谱图像. 为了减轻由于训练数据采样策略引起的预期偏差，测试了两种使用 RF 概率估计的技术以提高映射精度。1) 使用迭代和半自动主动学习技术来生成进一步的训练数据，并且 2) 应用了阈值移动相关的对象生长。这两种技术都有助于以 10 m 的空间分辨率制作整个瑞士的灌木森林地图。使用涵盖 7640 个定期分布的国家森林清单 (NFI) 地块的独立数据进行了准确性评估。我们观察了偏差校正技术的影响，并在每次执行迭代后发现了更高的准确度。预测的灌木林比例的平均绝对误差 (MAE) 从 6.04% 减少到 2.68%，同时实现接近 0 的平均偏差误差 (MBE)。本研究强调了将多传感器数据与偏差校正技术相结合以提供具有成本效益和准确的全国灌木林检测的潜力。此外，该地图补充了当前可用的 NFI 绘图样本点数据。